University of Ciego de Ávila Máximo Gómez Báez
|
ISSN: 2309-8333
|
RNPS: 2411
|13(2) |2025|
This is an Open Access article under the license CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)
Estrategia y Gestión Universitaria EGU
Review article
How to cite:
Supelano Londoño, M. L.
(2025). La Inteligencia Artificial como
aliada en la educación superior: más allá
del aula.
Estrategia y Gestión
Universitaria
, 13(2), e8968.
https://doi.org/10.5281/zenodo.17535089
Received: 20/08/2025
Accepted: 31/10/2025
Published: 10/11/2025
Corresponding author:
msupelanolo@uniminuto.edu.co
Conflict of interest:
the authors declare
that they have no conflict of interest,
which may have influenced the results
obtained or the proposed interpretations
.
Artificial Intelligence as an ally in
higher education: beyond the
classroom
La Inteligencia Artificial como aliada en
la educación superior: más allá del aula
Inteligência Artificial como aliada no
ensino superior: para além da sala de
aula
Abstract
Introduction: education has undergone a significant
transformation in both learning and teaching approaches.
Conceived as a fundamental process for human development,
it has begun to embrace emerging technologies, including
artificial intelligence, while upholding diversity and
strengthening the connection between educators, learners,
and knowledge. Objective: to analyze how artificial
intelligence has evolved from an emerging trend to an
essential tool within and beyond the classroom in higher
education, addressing its impact beyond traditional teaching
paradigms.
Methodology: an argumentative reflection was
conducted based on the observation of current cases involving
the use of artificial intelligence in university settings. This was
complemented by a literature review of publications from
2020 to 2025 and the use of support tools such as Jenni AI and
Scite AI to organize and explore the most relevant academic
sources. Results: various applications of artificial intelligence
in higher education were identified, including virtual
assistants, adaptive learning platforms, automated learning
algorithms, and predictive analytics. Conclusion: artificial
intelligence does not pose a threat, provided its
implementation adheres to ethical principles and takes into
account existing technological gaps.
Keywords: Artificial intelligence, higher education, learning,
classroom, technology
Resumen
Introducción: la educación ha experimentado un cambio
significativo en la manera de aprender como en la de enseñar.
Entendida como un proceso necesario para el desarrollo
humano ha comenzado a abrirse a nuevas tecnologías entre
ellas la inteligencia artificial, respetando la diversidad y la
consolidación del vínculo entre docentes, estudiantes y
saberes.
Mary Lizeth Supelano Londoño
1
Corporación Universitaria Minuto de Dios
UNIMINUTO
https://orcid.org/0009-0003-2560-7490
msupelanolo@uniminuto.edu.co
Colombia
Estrategia y Gestión Universitaria
|
ISSN
: 2309-8333
|
RNPS:
2411
13(2) | July-December |2025|
| Mary Lizeth Supelano Londoño |
Objetivo:
analizar cómo la inteligencia artificial ha dejado de ser una tendencia
emergente para convertirse en una herramienta esencial dentro y fuera del aula
en la educación superior, abordando su impacto más allá de la enseñanza
tradicional.
Metodología:
se realizó una reflexión argumentativa a basada en la
observación de casos actuales de uso de inteligencia artificial en el contexto
universitario, completada con una revisión bibliográfica de literatura publicada
entre 2020 y 2025, y el uso de herramientas de apoyo como Jenni IA y Scite AI
para organizar y explorar las fuentes académicas más relevantes.
Resultados:
se
identificaron diferentes usos de la inteligencia artificial en la educación superior,
la inclusión de asistentes virtuales, plataformas de aprendizaje adaptativo,
algoritmos de aprendizaje automatizado y análisis predictivo.
Conclusión:
la
inteligencia artificial no representa una amenaza, siempre que su
implementación respete los principios éticos y considere las brechas tecnológicas
existentes.
Palabras clave:
Inteligencia artificial, educación superior, aprendizaje, aula,
tecnología
Resumo
Introdução: a educação passou por uma transformação significativa tanto nas
formas de aprender quanto de ensinar. Compreendida como um processo essencial
para o desenvolvimento humano, começou a incorporar novas tecnologias, entre
elas a inteligência artificial, respeitando a diversidade e fortalecendo o vínculo
entre docentes, discentes e saberes. Objetivo: analisar como a inteligência
artificial deixou de ser uma tendência emergente para se tornar uma ferramenta
essencial dentro e fora da sala de aula no ensino superior, abordando seu impacto
para além da educação tradicional. Metodologia: foi realizada uma reflexão
argumentativa baseada na observação de casos atuais de uso da inteligência
artificial no contexto universitário, complementada por uma revisão bibliográfica
de literatura publicada entre 2020 e 2025, além da utilização de ferramentas de
apoio como Jenni IA e Scite AI para organizar e explorar as fontes acadêmicas mais
relevantes. Resultados: foram identificadas diversas aplicações da inteligência
artificial no ensino superior, incluindo assistentes virtuais, plataformas de
aprendizagem adaptativa, algoritmos de aprendizagem automatizada e análise
preditiva. Conclusão: a inteligência artificial não representa uma ameaça, desde
que sua implementação respeite os princípios éticos e considere as desigualdades
tecnológicas existentes.
Palavras-chave:
Inteligência artificial, ensino superior, aprendizagem, sala de
aula, tecnologia
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Introduction
In recent decades, we have witnessed significant transformations in the ways
humanity produces, circulates, and appropriates knowledge (Túñez-López et al.,
2019). The emergence of digital technologies, particularly Artificial Intelligence (AI),
represents a paradigm shift that impacts, among other areas, educational systems,
challenging traditional strategies of teaching, learning, assessment, and institutional
management (Bermeo-Paucar, 2024; Cisneros et al., 2023). Higher education, as a
space for generating advanced knowledge and scientific production, has not
remained unaffected by these transformations; rather, it serves as a privileged
venue for experimentation, implementation, and critical reflection on the effects of
AI in educational processes (Avellaneda-Callirgos et al., 2025; Gallifa et al., 2021).
Today, much of the literature and public debate tends to focus almost
exclusively on the use of AI related to the classroom, overlooking the multiple ways
in which this technology comprehensively transforms the university ecosystem
(Donayre Bohabot et al., 2024). The article presented here aims to consider Artificial
Intelligence as an agent of structural change in higher education, extending beyond
didactic use to lines of work related to academic management, student support,
scientific research, institutional governance, and strategic planning. AI transforms
higher education, impacting far beyond the traditional classroom.
Recent research explores how AI can enhance teaching, learning,
administration, and student support while presenting challenges and opportunities
for the future of universities. According to Nimbalagundi et al. (2024), AI has
demonstrated its potential to transform numerous fields, and education is no
exception. In the same vein, González (2023) argues that this tool is powerful and is
generating a revolution in the way teaching and learning occur. AI is a technology
that drives education at all levels, from early childhood to higher education, showing
positive advancements in teaching, although caution is currently warranted
regarding interactions with these technological applications. This overview of AI-
generated (GenAI) tools for higher education, such as ChatGPT, MidJourney, or
Codex, also highlights "AI-driven learning." AI technologies, including natural
language processing (NLP), automated performance enhancement (APE), intelligent
tutoring systems (ITS), personalized learning systems (PLS), virtual reality (VR), and
augmented reality (AR), can be applied across various areas of teaching, learning,
research, and societal service. According to Porayska-Pomsta et al. (2022), these
applications offer significant potential to transform educational processes.
Historically observing AI, we note that university systems have undergone
many stages; initially, it was not a promising prospect. Earlier technologies
preceding AI, such as basic computing or virtual learning environments, showed
important changes in teaching modalities. AI goes further by incorporating the ability
to learn, adapt, and make decisions based on patterns derived from massive data
analysis (Marr, 2020). This quality renders AI a technology endowed with agency,
capable of mediating academic, administrative, and scientific processes in real-time
and simultaneously redesigning institutional practices, thus posing new
epistemological, ethical, and political challenges.
One of the most evident contributions of AI in the realm of higher education
is its ability to adapt learning, personalizing diverse content and various pedagogical
| Mary Lizeth Supelano Londoño |
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strategies to the characteristics of each student. Platforms based on machine
learning algorithms can detect behavioral patterns, reinforcement needs, or specific
cognitive strengths, providing timely feedback and personalized guidance (Luckin et
al., 2016). This capability has proven to be not only adaptive but also inclusive,
generating concern and fear regarding the misuse of private data, the opacity of
algorithms, and the risk of reproducing structural biases (Williamson & Eynon, 2020).
AI is not only articulated through the pedagogical dimension but also subtly yet
substantially modifies academic and administrative management processes. Various
universities have begun to integrate institutional intelligence systems that, through
the collection and analysis of massive data, allow for anticipating trends, identifying
dropout risks, optimizing resource usage, or designing more efficient strategic
interventions (Daniel, 2015).
Scientific research has been impacted by AI, facilitating the systematic
organization of data, automated literature review, experimental design, and result
prediction. In fields such as biomedicine, economics, or engineering, researchers
have incorporated algorithmic models to analyze complex databases, identify
correlations, and generate hypotheses with a level of precision and speed
unattainable through traditional methods (Jordan & Mitchell, 2015).
While these tools amplify the evolutionary capabilities of academic workers,
they also raise questions about the boundaries that demarcate human authorship
and technical intervention, challenging conventional notions of creativity,
originality, and research ethics. The implementation of AI in education enables
personalized learning experiences and provides students with content and feedback
tailored to their individual needs and learning styles (López et al., 2023). This
personalization fosters student engagement and enhances learning outcomes by
addressing specific knowledge gaps and learning preferences (Dey et al., 2025).
In this context, the use of AI in higher education highlights epistemological
and pedagogical tensions that cannot be overlooked. From an epistemological
perspective, we must question how knowledge is produced through automated
models: Is AI an ally in higher education? This is a reflection we must confront. From
a pedagogical standpoint, we need to learn how to interact with the tools that
technology offers, training both educators and students in the new roles they must
establish with AI in a context where intelligent technologies mediate, ensuring that
the use of AI does not exacerbate inequalities or undermine the principles of
educational justice. Ethically, there must be normative frameworks and guiding
principles regarding data usage, algorithm transparency, and the establishment of
human-centered AI.
In light of this landscape, the present article aims to construct a broad,
rigorous, and proactive analysis of the role of Artificial Intelligence as a strategic
ally in the transformation of higher education. We start from a holistic conception
of the educational phenomenon, recognizing the interdependence between the
pedagogical dimension, the institutional dimension, the technological dimension,
and the cultural dimension. Through a review of updated literature, the study
analyzes concrete experiences of AI implementation in universities from different
parts of the world, identifies emerging opportunities and risks, and ultimately
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proposes a set of recommendations for the ethical, critical, and consistent
integration of these technologies in higher education.
Methodology
The methodology of this review is framed within a qualitative approach, in
accordance with the proposals of Gómez (2012), who defines it as “the discipline
that is responsible for the critical study of the procedures and means applied by
humans to achieve and create knowledge in the field of scientific research.”
Consistent with this, the methodology focuses on understanding complex social
phenomena from the participants' perspectives, aiming to explore meanings,
experiences, and perceptions through content analysis (Mativi et al., 2020).
To address the research question, a list of inclusion and exclusion criteria
was generated prior to conducting the bibliographic search. The inclusion criteria
focused on documents (books and articles) that discuss the impact of artificial
intelligence on higher education and that had been published between 2020 and 2025
(see Table 1).
Table 1
Eligibility criteria
Criterion Description
Year of
Publication
Articles or documents published between
2015 and
2025 were selected.
Language
Only articles or documents written in English or
Spanish were included.
Type of
Document
Only peer-
reviewed articles, collections of web
pages, books and book chapters, and peer-reviewed
conference papers were considered.
Thematic
Relevance
• Studies must specifically address the impact
of AI on higher education.
• Expansion of educational impact.
• Optimization of institutional processes.
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• Transformation of the teaching and student
roles.
• Contribution to educational quality and
equity.
• Response to contemporary challenges.
Text
Accessibility
Only the documents that were accessible were taken
into account, under the presumption of full access in
the complete format.
Study Quality
The quality was assessed
using the Mixed Methods
Appraisal Tool (MMAT), and only high-scoring studies
were included.
Source: Author’s own elaboration.
As exclusion criteria, articles from journals without an ISSN (International
Standard Serial Number) or those that did not meet the quality evaluation
established by the Mixed Methods Appraisal Tool (MMAT) were eliminated. The
MMAT, developed by Honga et al. (2019), includes five criteria for evaluating the
quality of qualitative studies, randomized controlled trials, non-randomized studies,
descriptive quantitative studies, and mixed methods studies. Including this
evaluation criterion ensured that only high-quality academic articles were included
in the study.
The bibliographic search was conducted between January and June 2025.
The databases used included Scopus, Web of Science, Erih Plus, Scielo, and Dialnet.
Keywords in both Spanish and English were employed, such as "inteligencia artificial
en educación superior," "Machine Learning en universidades," "impacto de IA en el
aprendizaje universitario," "Artificial Intelligence in higher education," "AI in
university learning," "beyond the classroom," and "Inteligencia Artificial en la
Educación Superior: Aplicaciones y Perspectivas".
This approach allowed for the identification of a total of 163 documents that
met the inclusion criteria. After a thorough review, 31 articles were selected,
classified, and interpreted, utilizing the PRISMA statement (Page et al., 2021).
Figure 1
Flow diagram based on the PRISMA statement
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Source: Author’s own elaboration.
To evaluate the quality of the selected studies, a checklist based on standard
methodological criteria was applied, considering both internal and external validity,
clear presentation of methods and results, and the relevance of the study to the
research question. Each study was assigned a quality score, and only those achieving
high ratings were included in the final analysis. This qualitative assessment ensures
Identification of studies through databases and records
Identified records from:
Scopus (n = 189)
ERIC (n = 103)
Web of Science (n = 59)
Scielo (n = 76)
Dialnet (n = 130)
Duplicates removed: 185
Records remaining for title/abstract screening:
372
Records excluded due to thematic irrelevance or
lack of focus on higher education: 280
Articles selected for full-text review: 92
Identification
Check
Inclusion
Records screened (n = 195)
Retrieval requested (n = 163)
Studies included in the review:
Studies published between 2020 and 2025
Language: English or Spanish
Peer
-reviewed articles and conference
papers, books, and book chapters
Studies conducted in higher education
contexts worldwide
Documents available in full text
Articles included in the final systematic
review: n = 31
Excluded studies:
Studies published before 2015
Articles published in languages other than English or
Spanish
Opinion pieces, editorials, conference abstracts, and
non-peer-reviewed theses
Studies that do not specifically address artificial
intelligence in higher education
Documents not accessible in full text
Studies that do not meet quality criteria according to
the Mixed Methods Appraisal Tool (MMAT)
Records excluded after full-text assessment (n =
132)
Full
-text articles assessed for eligibility: n = 92
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that the findings of the review possess a level of confidence that allows for the
contribution of relevant insights to the field of artificial intelligence in higher
education.
Following this, a synthesis of the data extracted was conducted using a
thematic approach, which reveals trends and common patterns in the findings among
the reviewed studies, as well as comparative benchmarks based on the type of AI
used and the educational context. The results are presented in a narrative format,
guided by tables and figures that summarize the findings.
The integration of AI in higher education not only optimizes educational
processes but also opens new avenues for pedagogical innovation and continuous
improvement (Ojha et al., 2023). The use of AI is essential for enhancing learning
and teaching (Ayala-Pazmiño, 2023).
Results and discussion
The results of this systematic literature review (SLR) are classified into
different emerging categories that have surfaced following the analysis of the 27
publications selected for this review. These emerging categories represent the main
findings of the research, linked to the inclusion of artificial intelligence in higher
education, describing both the advantages and the ethical and practical issues that
have been uncovered. A synthesis of the reviewed articles is presented, along with
the categorization of the emerging findings.
The academic and administrative role of artificial intelligence in
education
According to Ahmad et al. (2022), teachers are responsible for instruction in
any educational setting. However, they undertake additional tasks. Aside from
academic responsibilities, most of educators' time and resources are devoted to
administrative tasks. Mendieta Lucas et al. (2025) and Álvarez-Carrión et al. (2025)
agree that AI applications not only assist in academic and administrative education
but also enhance their effectiveness. AI provides support to educators in various
tasks, such as Learning Analytics (LA), Virtual Reality (VR), Grading/Evaluation
(G/A), and Admissions. It minimizes the administrative workload on teachers,
allowing them to focus more on teaching and student guidance.
Therefore, AI facilitates the automation of administrative tasks and the
provision of immediate feedback (Trávez Tipan, 2024). This frees up time for
educators to concentrate on teaching and curriculum development, thereby
improving the quality of education. AI platforms can offer personalized feedback on
student performance, identify areas for improvement, and suggest additional
resources to strengthen their skills (Tarisayi, 2023). However, the adoption of AI in
higher education is not without challenges.
Among the primary obstacles are ethical and privacy concerns, as well as the
necessity to ensure that the implementation of these technologies does not
perpetuate existing biases or exclude marginalized groups (Zhang, 2023). It is
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essential for institutions to develop clear and transparent policies to guide the use
of AI and ensure its ethical and equitable application (Spivakovsky et al., 2023; Tao
et al., 2021).
The use of artificial intelligence in an academic environment
AI is a branch of computer science focused on creating intelligent machines
capable of simulating human intelligence, behavior, and learning (Márquez
Benavides et al., 2023; Oliver, 2021). The academic community has not remained
immune to the allure of AI. The purpose of this work is to analyze and discuss the
implications of AI in academic environments, allowing readers to engage in a critical
debate regarding the use of artificial intelligence in an academic setting. AI has the
potential to accelerate scientific discoveries, open doors to knowledge, and
facilitate advancements in various fields of study (Maceri & Coll, 2024; Rodríguez
Padilla et al., 2024). Public universities can harness the power of AI to enhance
research capabilities and personalize education (Márquez Benavides et al., 2023).
The integration of artificial intelligence technologies in higher education has
enabled significant personalization of learning. According to Rodríguez-Chávez
(2021) and Jaramillo (2024), the use of Machine Learning and Deep Learning
algorithms has allowed for adaptation to the individual needs of students, thereby
enhancing their academic potential.
In this regard, educators are attempting to experiment with these tools by
modifying the types of tasks they assign; this is referred to as "emerging pedagogies."
While AI can assist in educational contexts, it is crucial for educators to understand
these systems so they can make informed decisions, and if they choose to integrate
them, do so within a framework of coherent didactic strategies (Sánchez Vera,
2024).
Artificial intelligence in the context of teacher training
The new information society presents significant challenges that, in turn,
demand substantial changes in the training frameworks of educational institutions.
AI offers a wide range of benefits in education but also presents a series of
challenges. At the higher education level, there is an urgent need for planning,
designing, developing, and implementing digital competencies, with the aim of
training and graduating better professionals who can compete in technological
environments (Ayala-Pazmiño, 2023).
AI provides powerful tools to adapt teaching to the individual needs of
students, thereby enhancing the effectiveness and overall educational experience
(Cobos Velasco, 2023; Meza Arguello et al., 2025). Recent studies highlight that AI
in virtual classrooms of higher education can facilitate continuous tracking of student
progress, allowing for faster feedback and early identification of learning difficulties
(Cherrez Escobar et al., 2024; Mar Cornelio et al., 2024). Additionally, AI in
personalized learning has been shown to promote the creation of more inclusive
educational environments, effectively addressing the diverse needs of students
(Ortiz Aguilar et al., 2024). While this technological advancement poses ethical and
privacy challenges, particularly regarding the collection and use of personal data
(Velasco, 2023).
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Reflection and collaboration in educational environments
The teacher, as a critical and reflective practitioner, must participate in the
creation of pedagogical knowledge and in decision-making processes regarding
educational practices. Early experiences in the classroom constitute a key period in
the professional development of teachers (Gómez, 2009).
The implementation of AI in higher education presents significant
challenges, including ethical issues, privacy concerns, and the need to reform
academic policies to ensure educational integrity and quality (Bennett, 2023). The
risks associated with the adoption of AI in education include potential job losses and
biases inherent in automated systems (George & Wooden, 2023).
Practical and ethical recommendations for using AI
When implementing AI in any domain, especially higher education, it is
essential to consider practical and ethical considerations to maximize the benefits
of AI tools while minimizing the risks associated with their use
The following recommendations are based on current research:
• Practical recommendations
Artificial intelligence has evolved significantly since its origins in the 1950s,
when Turing and other pioneers raised questions about the ability of machines to
think. Its development has transformed numerous fields, including education, where
AI-based technologies have revolutionized teaching and learning methods, allowing
for educational personalization and optimization (Alcocer, 2024). AI has proven to
be a promising tool in education, with significant implications for how teaching and
learning occur. The inclusion of AI in education has provided an impetus for the
recommendation of educational content, enabling personalized and efficient
learning (León Granizo, 2024). However, it is important to recognize that AI does not
operate independently; its implementation in higher education includes several key
aspects. It is crucial to provide adequate training for educators and administrative
staff, not only in the technical use of AI platforms but also in their pedagogical
integration within the classroom (Younas et al., 2023).
• Ethical recommendations
AI has emerged as a powerful tool in both scientific and social fields,
enhancing education and research through new methods of teaching and access to
knowledge. However, its adoption poses significant ethical and moral challenges,
such as the need to address transparency, biases, and discrimination (Cárdenas,
2024). The use of AI in education from an ethical perspective should consider its
impact on educational contexts as a breeding ground for the ethical and political
challenges faced by society. This allows for an understanding of its scope and depth,
proposing measures to address these challenges (Rodríguez, 2024). The
implementation of AI in higher education must adhere to ethical and transparent
principles to maximize its benefits and minimize risks. The algorithms used must be
transparent and auditable, enabling students and academic staff to understand how
automated decisions are made and to question and correct possible errors (Agbese
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et al., 2023).
As previously indicated, the results obtained from this literature review
provide a comprehensive view of the integration of AI in higher education. The
following sections will interpret the results and discuss how they differ from previous
research, if applicable, and what implications they hold for education.
Comparison with previous studies
The findings of this review align with and expand the existing literature on
the integration of AI in higher education, demonstrating that AI is changing the
university environment. This change is evident not only in the pedagogical domain
but also in academic management, scientific research within educational
institutions, student support, and institutional decision-making. This structural
transformation transcends traditional educational approaches and invites a critical
examination of the role of technology in the educational process.
Among the most notable findings is AI's ability to promote personalized
teaching. Platforms driven by machine learning algorithms allow for interaction and
the creation of adaptive content tailored to each student's needs. This represents a
step toward a more inclusive and student-centered education, as suggested by López
et al. (2023) and Dey et al. (2025). However, concerns arise regarding unethical
practices related to data privacy, algorithm transparency, and the potential
perpetuation of structural biases (Williamson & Eynon, 2020).
Scientific research is increasingly vulnerable as AI enables the processing of
large volumes of information, identifying complex correlations and generating
hypotheses with greater precision. However, this capability raises questions about
human authorship and presents new epistemological and ethical challenges,
particularly regarding the originality of produced knowledge (Jordan & Mitchell,
2015; Marr, 2020). From a qualitative perspective, this article helps to understand
the opportunities and threats emerging from the use of AI in educational contexts.
While AI is viewed as a strategic ally for educational enhancement, its usage must
be critical, ethical, and contextualized. It is not merely about adopting new
technological tools but about rethinking their use, the paradigms under which they
operate, educational justice, and continuous improvement.
Ethical and practical challenges
Despite the numerous advantages, the implementation of artificial
intelligence in higher education poses ethical and practical issues. Data privacy risks
and algorithmic bias are essential concerns that must be addressed (Quinto Ochoa,
2024; Gallent-Torres et al., 2024). From this perspective, the lack of integrated
training for teaching staff and insufficient technological infrastructure are practical
obstacles that could hinder the effective implementation of AI (Hemachandran et
al., 2022). These challenges highlight the need to create clear regulations and
policies to ensure the proper use of AI in educational environments.
Practical and ethical recommendations
For successful implementation, a series of practical and ethical
recommendations should be followed. First, teaching and administrative staff must
receive adequate training to effectively manage all the aforementioned tools. In
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addition, universities should invest in increasing technological infrastructure to
support these initiatives (Varela, 2023).
From an ethical standpoint, it is essential to develop algorithms that are
used transparently and are auditable; this way, risks can be minimized, and it can
be ensured that automated decisions are fair and equitable. Educators should be
involved in the AI implementation process, as it will be an ally in organizing classes
and other activities with students. It is crucial that technologies adapt to the real
needs of both students and teachers.
Implications for future research
The findings of this review highlight the need to continue research on the
use of AI in various educational settings, particularly in areas that still present
significant challenges. Future research should focus on developing and testing
different methodologies used for the implementation of AI in educational contexts,
considering important aspects such as student diversity, cultural and religious
differences, and technological infrastructure.
Conclusions
This literature review examines how artificial intelligence can be integrated
responsibly and equitably within educational settings, aiming to maximize its
positive effects while minimizing the negative ones. The results of this research
demonstrate AI's capacity to adapt learning and enhance students' academic
performance. These findings align with previous studies that establish its efficacy as
an educational tool while identifying new impacts, such as improvements in school
management and the promotion of a humanistic approach to education.
Among the benefits of using artificial intelligence is its ability to generate
personalized teaching methods tailored to students' needs, leading to better
academic performance. AI acts as an automator of administrative tasks, freeing
educators from operational activities and allowing them to focus on curriculum
development and instructional quality. Furthermore, its implementation facilitates
immediate and personalized feedback, enhancing learning acquisition and fostering
collaborative and interactive environments based on augmented and virtual
realities, significantly increasing students' understanding and retention of
knowledge.
In the broader context of educational research, these elements align with
the growing digitalization and personalization of learning. The comparison with other
reviewed studies indicates coherence in describing the advancements achievable
through artificial intelligence, as well as in identifying the ethical and practical
challenges that must be addressed for effective implementation. Artificial
intelligence offers the potential to transform higher education, provided it is used
cautiously and measures are taken to avoid biases, thus ensuring equitable access
for all students.
Recognizing the limitations of the study, it is relevant to note that the review
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is based on research published between 2020 and 2025, which may exclude some
pertinent work due to temporal constraints. Nevertheless, this work opens new lines
of inquiry and may inspire future researchers to explore how artificial intelligence
continues to position itself as a valuable and just ally in the educational field.
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| Mary Lizeth Supelano Londoño |
About the main author
Mary Lizeth Supelano Londoño:
Master's degree in Educational Technology and
Digital Competencies from the International University of La Rioja (UNIR). Specialist
in Project Management and professional in Financial Administration, both degrees
awarded by the Minuto de Dios University Corporation. Software Analysis and
Development Technologist from SENA. Currently, he serves as a research group
leader at the Minuto de Dios University
.
Declaration of author responsibility
Mary Lizeth Supelano Londoño
: 1:
Conceptualization, Data curation, Formal
analysis, Research, Methodology, Resources, Software, Supervision,
Validation/Verification, Visualization, Writing/original draft and Writing, review and
editing.
Financing:
Own resources.
Special Acknowledgments:
